Overview

Dataset statistics

Number of variables20
Number of observations1081784
Missing cells72736
Missing cells (%)0.3%
Duplicate rows1405
Duplicate rows (%)0.1%
Total size in memory173.3 MiB
Average record size in memory168.0 B

Variable types

Text4
Categorical4
DateTime5
Numeric7

Alerts

Dataset has 1405 (0.1%) duplicate rowsDuplicates
Team has 21129 (2.0%) missing valuesMissing
Status has 16781 (1.6%) missing valuesMissing
TripDuration is highly skewed (γ1 = 348.0823194)Skewed

Reproduction

Analysis started2024-04-16 08:08:35.779488
Analysis finished2024-04-16 08:09:14.294004
Duration38.51 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Distinct177708
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:14.571004image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters9736056
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15705 ?
Unique (%)1.5%

Sample

1st rowTL9008453
2nd rowTL9008453
3rd rowTL9009105
4th rowTL9009105
5th rowTL9008550
ValueCountFrequency (%)
xe7047245 193
 
< 0.1%
tl9031430 161
 
< 0.1%
xe7003986 156
 
< 0.1%
xe7010523 153
 
< 0.1%
xe7009115 143
 
< 0.1%
xe7005058 143
 
< 0.1%
xe7041124 139
 
< 0.1%
xe7008614 139
 
< 0.1%
te8016426 135
 
< 0.1%
te8009655 135
 
< 0.1%
Other values (177698) 1080287
99.9%
2024-04-16T16:09:15.024191image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1811201
18.6%
7 896590
9.2%
9 854228
8.8%
1 680071
 
7.0%
2 658163
 
6.8%
E 638308
 
6.6%
T 619934
 
6.4%
8 616632
 
6.3%
3 614807
 
6.3%
4 546297
 
5.6%
Other values (5) 1799825
18.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9736056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1811201
18.6%
7 896590
9.2%
9 854228
8.8%
1 680071
 
7.0%
2 658163
 
6.8%
E 638308
 
6.6%
T 619934
 
6.4%
8 616632
 
6.3%
3 614807
 
6.3%
4 546297
 
5.6%
Other values (5) 1799825
18.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9736056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1811201
18.6%
7 896590
9.2%
9 854228
8.8%
1 680071
 
7.0%
2 658163
 
6.8%
E 638308
 
6.6%
T 619934
 
6.4%
8 616632
 
6.3%
3 614807
 
6.3%
4 546297
 
5.6%
Other values (5) 1799825
18.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9736056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1811201
18.6%
7 896590
9.2%
9 854228
8.8%
1 680071
 
7.0%
2 658163
 
6.8%
E 638308
 
6.6%
T 619934
 
6.4%
8 616632
 
6.3%
3 614807
 
6.3%
4 546297
 
5.6%
Other values (5) 1799825
18.5%

CUSTID
Text

Distinct94
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:15.183202image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.0000009
Min length8

Characters and Unicode

Total characters8654273
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row00-RE001
2nd row00-RE001
3rd row00-RE001
4th row00-RE001
5th row00-RE001
ValueCountFrequency (%)
00-rp036 207563
19.2%
00-re001 174616
16.1%
00-rc044 168571
15.6%
00-rs035 109141
10.1%
00-rs174 58623
 
5.4%
00-rs020 48087
 
4.4%
00-ri046 44040
 
4.1%
00-re111 39215
 
3.6%
00-re006 33287
 
3.1%
00-rb089 33285
 
3.1%
Other values (84) 165356
15.3%
2024-04-16T16:09:15.440140image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3364737
38.9%
- 1081784
 
12.5%
R 1072042
 
12.4%
4 470843
 
5.4%
1 433871
 
5.0%
6 370173
 
4.3%
3 346585
 
4.0%
E 284255
 
3.3%
P 262974
 
3.0%
S 226436
 
2.6%
Other values (22) 740573
 
8.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8654273
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3364737
38.9%
- 1081784
 
12.5%
R 1072042
 
12.4%
4 470843
 
5.4%
1 433871
 
5.0%
6 370173
 
4.3%
3 346585
 
4.0%
E 284255
 
3.3%
P 262974
 
3.0%
S 226436
 
2.6%
Other values (22) 740573
 
8.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8654273
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3364737
38.9%
- 1081784
 
12.5%
R 1072042
 
12.4%
4 470843
 
5.4%
1 433871
 
5.0%
6 370173
 
4.3%
3 346585
 
4.0%
E 284255
 
3.3%
P 262974
 
3.0%
S 226436
 
2.6%
Other values (22) 740573
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8654273
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3364737
38.9%
- 1081784
 
12.5%
R 1072042
 
12.4%
4 470843
 
5.4%
1 433871
 
5.0%
6 370173
 
4.3%
3 346585
 
4.0%
E 284255
 
3.3%
P 262974
 
3.0%
S 226436
 
2.6%
Other values (22) 740573
 
8.6%

JOBTYPE
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
EXPORT
638285 
LOCAL
300671 
IMPORT
142828 

Length

Max length6
Median length6
Mean length5.72206
Min length5

Characters and Unicode

Total characters6190033
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLOCAL
2nd rowLOCAL
3rd rowLOCAL
4th rowLOCAL
5th rowLOCAL

Common Values

ValueCountFrequency (%)
EXPORT 638285
59.0%
LOCAL 300671
27.8%
IMPORT 142828
 
13.2%

Length

2024-04-16T16:09:15.571806image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:09:15.676847image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
export 638285
59.0%
local 300671
27.8%
import 142828
 
13.2%

Most occurring characters

ValueCountFrequency (%)
O 1081784
17.5%
P 781113
12.6%
R 781113
12.6%
T 781113
12.6%
E 638285
10.3%
X 638285
10.3%
L 601342
9.7%
C 300671
 
4.9%
A 300671
 
4.9%
I 142828
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6190033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
O 1081784
17.5%
P 781113
12.6%
R 781113
12.6%
T 781113
12.6%
E 638285
10.3%
X 638285
10.3%
L 601342
9.7%
C 300671
 
4.9%
A 300671
 
4.9%
I 142828
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6190033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
O 1081784
17.5%
P 781113
12.6%
R 781113
12.6%
T 781113
12.6%
E 638285
10.3%
X 638285
10.3%
L 601342
9.7%
C 300671
 
4.9%
A 300671
 
4.9%
I 142828
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6190033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
O 1081784
17.5%
P 781113
12.6%
R 781113
12.6%
T 781113
12.6%
E 638285
10.3%
X 638285
10.3%
L 601342
9.7%
C 300671
 
4.9%
A 300671
 
4.9%
I 142828
 
2.3%

Department
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
XINHUA
461826 
CHIEN
439838 
MAIN
180096 
EXPRESS
 
24

Length

Max length7
Median length6
Mean length5.2604753
Min length4

Characters and Unicode

Total characters5690698
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHIEN
2nd rowCHIEN
3rd rowCHIEN
4th rowCHIEN
5th rowCHIEN

Common Values

ValueCountFrequency (%)
XINHUA 461826
42.7%
CHIEN 439838
40.7%
MAIN 180096
 
16.6%
EXPRESS 24
 
< 0.1%

Length

2024-04-16T16:09:16.032505image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:09:16.148079image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
xinhua 461826
42.7%
chien 439838
40.7%
main 180096
 
16.6%
express 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
I 1081760
19.0%
N 1081760
19.0%
H 901664
15.8%
A 641922
11.3%
X 461850
8.1%
U 461826
8.1%
E 439886
7.7%
C 439838
7.7%
M 180096
 
3.2%
S 48
 
< 0.1%
Other values (2) 48
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5690698
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 1081760
19.0%
N 1081760
19.0%
H 901664
15.8%
A 641922
11.3%
X 461850
8.1%
U 461826
8.1%
E 439886
7.7%
C 439838
7.7%
M 180096
 
3.2%
S 48
 
< 0.1%
Other values (2) 48
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5690698
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 1081760
19.0%
N 1081760
19.0%
H 901664
15.8%
A 641922
11.3%
X 461850
8.1%
U 461826
8.1%
E 439886
7.7%
C 439838
7.7%
M 180096
 
3.2%
S 48
 
< 0.1%
Other values (2) 48
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5690698
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 1081760
19.0%
N 1081760
19.0%
H 901664
15.8%
A 641922
11.3%
X 461850
8.1%
U 461826
8.1%
E 439886
7.7%
C 439838
7.7%
M 180096
 
3.2%
S 48
 
< 0.1%
Other values (2) 48
 
< 0.1%
Distinct487
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:16.389691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.3466089
Min length2

Characters and Unicode

Total characters9029228
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)< 0.1%

Sample

1st rowJI DRUM
2nd rowJI DRUM
3rd rowJI DRUM
4th rowJI DRUM
5th rowJI DRUM
ValueCountFrequency (%)
ptcisl 124135
 
7.5%
off 124127
 
7.5%
ptc 76467
 
4.6%
j.island 63829
 
3.9%
100 56924
 
3.5%
pam 56647
 
3.4%
g 47174
 
2.9%
isl 47172
 
2.9%
island(g 45435
 
2.8%
ptcmrtgr 40602
 
2.5%
Other values (495) 962558
58.5%
2024-04-16T16:09:16.739634image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 672190
 
7.4%
A 664151
 
7.4%
T 639609
 
7.1%
563286
 
6.2%
S 533659
 
5.9%
N 486815
 
5.4%
L 476693
 
5.3%
I 467112
 
5.2%
C 466250
 
5.2%
R 430838
 
4.8%
Other values (34) 3628625
40.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9029228
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 672190
 
7.4%
A 664151
 
7.4%
T 639609
 
7.1%
563286
 
6.2%
S 533659
 
5.9%
N 486815
 
5.4%
L 476693
 
5.3%
I 467112
 
5.2%
C 466250
 
5.2%
R 430838
 
4.8%
Other values (34) 3628625
40.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9029228
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 672190
 
7.4%
A 664151
 
7.4%
T 639609
 
7.1%
563286
 
6.2%
S 533659
 
5.9%
N 486815
 
5.4%
L 476693
 
5.3%
I 467112
 
5.2%
C 466250
 
5.2%
R 430838
 
4.8%
Other values (34) 3628625
40.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9029228
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 672190
 
7.4%
A 664151
 
7.4%
T 639609
 
7.1%
563286
 
6.2%
S 533659
 
5.9%
N 486815
 
5.4%
L 476693
 
5.3%
I 467112
 
5.2%
C 466250
 
5.2%
R 430838
 
4.8%
Other values (34) 3628625
40.2%
Distinct497
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:17.019367image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length17
Median length14
Mean length7.730779
Min length2

Characters and Unicode

Total characters8363033
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)< 0.1%

Sample

1st row1 SERAYA AVE
2nd row1 SERAYA AVE
3rd row1 SERAYA AVE
4th row1 SERAYA AVE
5th row1 SERAYA AVE
ValueCountFrequency (%)
ptcisl 93227
 
6.1%
off 93224
 
6.1%
ppt 76135
 
5.0%
ptc 72763
 
4.8%
j.island 63626
 
4.2%
100 56873
 
3.7%
pam 56603
 
3.7%
island(g 46150
 
3.0%
g 45422
 
3.0%
isl 45417
 
3.0%
Other values (508) 874769
57.4%
2024-04-16T16:09:17.415153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
P 859245
 
10.3%
T 701952
 
8.4%
A 676468
 
8.1%
S 542171
 
6.5%
442426
 
5.3%
N 401077
 
4.8%
I 398732
 
4.8%
L 392782
 
4.7%
R 368735
 
4.4%
C 363627
 
4.3%
Other values (34) 3215818
38.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8363033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
P 859245
 
10.3%
T 701952
 
8.4%
A 676468
 
8.1%
S 542171
 
6.5%
442426
 
5.3%
N 401077
 
4.8%
I 398732
 
4.8%
L 392782
 
4.7%
R 368735
 
4.4%
C 363627
 
4.3%
Other values (34) 3215818
38.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8363033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
P 859245
 
10.3%
T 701952
 
8.4%
A 676468
 
8.1%
S 542171
 
6.5%
442426
 
5.3%
N 401077
 
4.8%
I 398732
 
4.8%
L 392782
 
4.7%
R 368735
 
4.4%
C 363627
 
4.3%
Other values (34) 3215818
38.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8363033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
P 859245
 
10.3%
T 701952
 
8.4%
A 676468
 
8.1%
S 542171
 
6.5%
442426
 
5.3%
N 401077
 
4.8%
I 398732
 
4.8%
L 392782
 
4.7%
R 368735
 
4.4%
C 363627
 
4.3%
Other values (34) 3215818
38.5%
Distinct194248
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
Minimum2020-12-31 23:30:00
Maximum2023-12-31 21:20:00
2024-04-16T16:09:17.563784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:17.709340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

ENDTM
Date

Distinct191366
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
Minimum2021-01-01 00:00:00
Maximum2023-12-31 21:40:00
2024-04-16T16:09:17.849338image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:17.986341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TripDuration
Real number (ℝ)

SKEWED 

Distinct451
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8950775
Minimum0
Maximum625
Zeros5133
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:18.121341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.33333333
Q10.33333333
median0.83333333
Q31
95-th percentile2
Maximum625
Range625
Interquartile range (IQR)0.66666667

Descriptive statistics

Standard deviation1.3726842
Coefficient of variation (CV)1.5335925
Kurtosis158010.72
Mean0.8950775
Median Absolute Deviation (MAD)0.33333333
Skewness348.08232
Sum968280.52
Variance1.8842618
MonotonicityNot monotonic
2024-04-16T16:09:18.260340image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 258761
23.9%
0.3333333333 252116
23.3%
0.5 220585
20.4%
1.5 107049
9.9%
2 61476
 
5.7%
0.1666666667 25325
 
2.3%
0.6666666667 14718
 
1.4%
0.8333333333 12510
 
1.2%
2.5 9164
 
0.8%
1.333333333 7781
 
0.7%
Other values (441) 112299
10.4%
ValueCountFrequency (%)
0 5133
 
0.5%
0.1666666667 25325
2.3%
0.1833333333 13
 
< 0.1%
0.2 20
 
< 0.1%
0.2166666667 33
 
< 0.1%
0.2333333333 53
 
< 0.1%
0.25 2935
 
0.3%
0.2666666667 106
 
< 0.1%
0.2833333333 108
 
< 0.1%
0.3 158
 
< 0.1%
ValueCountFrequency (%)
625 4
 
< 0.1%
48.5 4
 
< 0.1%
25.81666667 2
 
< 0.1%
25 10
< 0.1%
24.75 8
< 0.1%
24.5 6
< 0.1%
24.33333333 2
 
< 0.1%
24.25 1
 
< 0.1%
24.16666667 3
 
< 0.1%
16.5 1
 
< 0.1%
Distinct1095
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
Minimum2021-01-01 00:00:00
Maximum2023-12-31 00:00:00
2024-04-16T16:09:18.405341image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:18.595614image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

DriverId
Real number (ℝ)

Distinct199
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.77487
Minimum0
Maximum594
Zeros5133
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:18.740286image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41
Q1185
median301
Q3384
95-th percentile508
Maximum594
Range594
Interquartile range (IQR)199

Descriptive statistics

Standard deviation144.08675
Coefficient of variation (CV)0.49723688
Kurtosis-0.8071874
Mean289.77487
Median Absolute Deviation (MAD)97
Skewness-0.19430128
Sum3.1347381 × 108
Variance20760.992
MonotonicityNot monotonic
2024-04-16T16:09:18.881287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
220 32603
 
3.0%
382 22678
 
2.1%
128 19415
 
1.8%
88 17690
 
1.6%
293 16801
 
1.6%
74 16623
 
1.5%
326 14611
 
1.4%
330 14273
 
1.3%
375 13905
 
1.3%
302 13763
 
1.3%
Other values (189) 899422
83.1%
ValueCountFrequency (%)
0 5133
0.5%
2 499
 
< 0.1%
4 8935
0.8%
28 984
 
0.1%
29 8824
0.8%
33 12062
1.1%
36 5617
0.5%
37 6365
0.6%
41 10070
0.9%
51 6870
0.6%
ValueCountFrequency (%)
594 73
 
< 0.1%
592 643
 
0.1%
591 810
 
0.1%
589 714
 
0.1%
588 767
 
0.1%
587 520
 
< 0.1%
586 700
 
0.1%
583 2260
0.2%
582 1023
0.1%
580 1686
0.2%

PrimeMoverId
Real number (ℝ)

Distinct199
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean293.43551
Minimum1
Maximum1002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:19.028287image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile103
Q1234
median304
Q3363
95-th percentile418
Maximum1002
Range1001
Interquartile range (IQR)129

Descriptive statistics

Standard deviation105.49956
Coefficient of variation (CV)0.35953237
Kurtosis10.523302
Mean293.43551
Median Absolute Deviation (MAD)66
Skewness1.0750358
Sum3.1743384 × 108
Variance11130.158
MonotonicityNot monotonic
2024-04-16T16:09:19.210100image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
222 32607
 
3.0%
384 17697
 
1.6%
234 15437
 
1.4%
257 15055
 
1.4%
333 14611
 
1.4%
392 13550
 
1.3%
332 13466
 
1.2%
258 12405
 
1.1%
383 12017
 
1.1%
218 11923
 
1.1%
Other values (189) 923016
85.3%
ValueCountFrequency (%)
1 9905
0.9%
2 3429
 
0.3%
10 4196
 
0.4%
13 10598
1.0%
17 1
 
< 0.1%
22 2312
 
0.2%
64 701
 
0.1%
69 815
 
0.1%
81 2730
 
0.3%
85 678
 
0.1%
ValueCountFrequency (%)
1002 39
 
< 0.1%
1001 5094
0.5%
1000 868
 
0.1%
484 1995
 
0.2%
483 578
 
0.1%
482 1046
 
0.1%
481 842
 
0.1%
480 1085
 
0.1%
465 3454
0.3%
450 2893
0.3%

TrailerId
Real number (ℝ)

Distinct1470
Distinct (%)0.1%
Missing6042
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean1006.0034
Minimum1
Maximum1970
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:19.404939image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile94
Q1635
median1009
Q31394
95-th percentile1819
Maximum1970
Range1969
Interquartile range (IQR)759

Descriptive statistics

Standard deviation523.33974
Coefficient of variation (CV)0.52021666
Kurtosis-0.8724849
Mean1006.0034
Median Absolute Deviation (MAD)378
Skewness-0.1644765
Sum1.0822001 × 109
Variance273884.48
MonotonicityNot monotonic
2024-04-16T16:09:19.554961image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1245 16470
 
1.5%
989 14793
 
1.4%
1132 13836
 
1.3%
1028 12549
 
1.2%
1243 10383
 
1.0%
900 10007
 
0.9%
1009 9856
 
0.9%
994 9750
 
0.9%
974 9052
 
0.8%
965 7772
 
0.7%
Other values (1460) 961274
88.9%
ValueCountFrequency (%)
1 738
0.1%
2 756
0.1%
3 1193
0.1%
4 499
< 0.1%
5 804
0.1%
6 599
0.1%
7 553
0.1%
8 569
0.1%
9 630
0.1%
10 730
0.1%
ValueCountFrequency (%)
1970 37
 
< 0.1%
1957 2
 
< 0.1%
1939 468
< 0.1%
1938 367
< 0.1%
1937 133
 
< 0.1%
1936 439
< 0.1%
1935 231
< 0.1%
1934 308
< 0.1%
1933 179
 
< 0.1%
1932 241
< 0.1%
Distinct176
Distinct (%)< 0.1%
Missing7196
Missing (%)0.7%
Memory size16.5 MiB
Minimum1980-01-01 00:00:00
Maximum2023-11-27 00:00:00
2024-04-16T16:09:19.688957image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:19.833491image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct71
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size16.5 MiB
Minimum2020-07-08 00:00:00
Maximum2023-12-31 00:00:00
2024-04-16T16:09:20.177179image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:20.336733image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

RACE
Categorical

Distinct6
Distinct (%)< 0.1%
Missing7196
Missing (%)0.7%
Memory size16.5 MiB
CHINESE
720872 
MALAY
166009 
MYANMAR
151659 
INDIAN
 
32717
MALIAN
 
3258

Length

Max length7
Median length7
Mean length6.6575497
Min length5

Characters and Unicode

Total characters7154123
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHINESE
2nd rowCHINESE
3rd rowCHINESE
4th rowCHINESE
5th rowCHINESE

Common Values

ValueCountFrequency (%)
CHINESE 720872
66.6%
MALAY 166009
 
15.3%
MYANMAR 151659
 
14.0%
INDIAN 32717
 
3.0%
MALIAN 3258
 
0.3%
Chinese 73
 
< 0.1%
(Missing) 7196
 
0.7%

Length

2024-04-16T16:09:20.476592image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-16T16:09:20.593590image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
chinese 720945
67.1%
malay 166009
 
15.4%
myanmar 151659
 
14.1%
indian 32717
 
3.0%
malian 3258
 
0.3%

Most occurring characters

ValueCountFrequency (%)
E 1441744
20.2%
N 941223
13.2%
I 789564
11.0%
C 720945
10.1%
S 720872
10.1%
H 720872
10.1%
A 674569
9.4%
M 472585
 
6.6%
Y 317668
 
4.4%
L 169267
 
2.4%
Other values (7) 184814
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7154123
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1441744
20.2%
N 941223
13.2%
I 789564
11.0%
C 720945
10.1%
S 720872
10.1%
H 720872
10.1%
A 674569
9.4%
M 472585
 
6.6%
Y 317668
 
4.4%
L 169267
 
2.4%
Other values (7) 184814
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7154123
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1441744
20.2%
N 941223
13.2%
I 789564
11.0%
C 720945
10.1%
S 720872
10.1%
H 720872
10.1%
A 674569
9.4%
M 472585
 
6.6%
Y 317668
 
4.4%
L 169267
 
2.4%
Other values (7) 184814
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7154123
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1441744
20.2%
N 941223
13.2%
I 789564
11.0%
C 720945
10.1%
S 720872
10.1%
H 720872
10.1%
A 674569
9.4%
M 472585
 
6.6%
Y 317668
 
4.4%
L 169267
 
2.4%
Other values (7) 184814
 
2.6%

Team
Categorical

MISSING 

Distinct15
Distinct (%)< 0.1%
Missing21129
Missing (%)2.0%
Memory size16.5 MiB
CHIEN LI
421062 
FCL
176098 
Team 5
113686 
Team 2
112589 
TRANSHIPMENT
112588 
Other values (10)
124632 

Length

Max length25
Median length19
Mean length7.1372671
Min length3

Characters and Unicode

Total characters7570178
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCHIEN LI
2nd rowCHIEN LI
3rd rowCHIEN LI
4th rowCHIEN LI
5th rowCHIEN LI

Common Values

ValueCountFrequency (%)
CHIEN LI 421062
38.9%
FCL 176098
16.3%
Team 5 113686
 
10.5%
Team 2 112589
 
10.4%
TRANSHIPMENT 112588
 
10.4%
Team 1 36416
 
3.4%
Team 4 31497
 
2.9%
Team 3 20683
 
1.9%
Team 6 18631
 
1.7%
M&R – SPARE TRUCK 14221
 
1.3%
Other values (5) 3184
 
0.3%
(Missing) 21129
 
2.0%

Length

2024-04-16T16:09:20.762948image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chien 421062
22.6%
li 421062
22.6%
team 333502
17.9%
fcl 176098
9.5%
5 113686
 
6.1%
2 112589
 
6.0%
transhipment 112588
 
6.0%
1 36416
 
2.0%
4 31497
 
1.7%
3 20683
 
1.1%
Other values (14) 82602
 
4.4%

Most occurring characters

ValueCountFrequency (%)
I 961643
12.7%
801130
10.6%
N 652842
8.6%
C 614651
 
8.1%
L 597649
 
7.9%
T 576797
 
7.6%
E 549857
 
7.3%
H 536356
 
7.1%
e 333502
 
4.4%
a 333502
 
4.4%
Other values (24) 1612249
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7570178
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
I 961643
12.7%
801130
10.6%
N 652842
8.6%
C 614651
 
8.1%
L 597649
 
7.9%
T 576797
 
7.6%
E 549857
 
7.3%
H 536356
 
7.1%
e 333502
 
4.4%
a 333502
 
4.4%
Other values (24) 1612249
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7570178
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
I 961643
12.7%
801130
10.6%
N 652842
8.6%
C 614651
 
8.1%
L 597649
 
7.9%
T 576797
 
7.6%
E 549857
 
7.3%
H 536356
 
7.1%
e 333502
 
4.4%
a 333502
 
4.4%
Other values (24) 1612249
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7570178
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
I 961643
12.7%
801130
10.6%
N 652842
8.6%
C 614651
 
8.1%
L 597649
 
7.9%
T 576797
 
7.6%
E 549857
 
7.3%
H 536356
 
7.1%
e 333502
 
4.4%
a 333502
 
4.4%
Other values (24) 1612249
21.3%

Status
Real number (ℝ)

MISSING 

Distinct63
Distinct (%)< 0.1%
Missing16781
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean13.734014
Minimum1
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:20.909137image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q18
median12
Q317
95-th percentile28
Maximum65
Range64
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.805027
Coefficient of variation (CV)0.56829904
Kurtosis5.0985979
Mean13.734014
Median Absolute Deviation (MAD)4
Skewness1.8234531
Sum14626766
Variance60.918446
MonotonicityNot monotonic
2024-04-16T16:09:21.055135image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 86636
 
8.0%
9 83782
 
7.7%
10 79576
 
7.4%
7 73314
 
6.8%
11 68602
 
6.3%
12 64428
 
6.0%
6 55303
 
5.1%
14 54823
 
5.1%
16 52762
 
4.9%
13 51833
 
4.8%
Other values (53) 393944
36.4%
ValueCountFrequency (%)
1 1061
 
0.1%
2 3639
 
0.3%
3 7476
 
0.7%
4 18542
 
1.7%
5 32781
 
3.0%
6 55303
5.1%
7 73314
6.8%
8 86636
8.0%
9 83782
7.7%
10 79576
7.4%
ValueCountFrequency (%)
65 130
 
< 0.1%
64 64
 
< 0.1%
62 62
 
< 0.1%
60 240
< 0.1%
59 236
< 0.1%
58 116
 
< 0.1%
57 171
 
< 0.1%
56 224
< 0.1%
55 440
< 0.1%
54 216
< 0.1%

AGE_Years
Real number (ℝ)

Distinct157
Distinct (%)< 0.1%
Missing7196
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean50.693303
Minimum24.25
Maximum76.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:21.208134image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum24.25
5-th percentile33.83
Q141.58
median51.33
Q358.25
95-th percentile67.67
Maximum76.33
Range52.08
Interquartile range (IQR)16.67

Descriptive statistics

Standard deviation10.790382
Coefficient of variation (CV)0.21285615
Kurtosis-0.85579687
Mean50.693303
Median Absolute Deviation (MAD)7.67
Skewness-0.068269717
Sum54474416
Variance116.43234
MonotonicityNot monotonic
2024-04-16T16:09:21.376870image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51.33 46876
 
4.3%
45.25 29992
 
2.8%
49.83 25967
 
2.4%
46.92 25301
 
2.3%
37.83 22905
 
2.1%
56.58 22678
 
2.1%
55.75 22373
 
2.1%
67.67 20036
 
1.9%
56.42 19429
 
1.8%
57.75 17690
 
1.6%
Other values (147) 821341
75.9%
ValueCountFrequency (%)
24.25 326
 
< 0.1%
25.67 133
 
< 0.1%
25.83 3571
0.3%
26.08 504
 
< 0.1%
26.92 1868
 
0.2%
27.42 4715
0.4%
28.92 1023
 
0.1%
29 859
 
0.1%
30.08 6831
0.6%
30.25 146
 
< 0.1%
ValueCountFrequency (%)
76.33 3063
 
0.3%
72.58 4005
0.4%
71.67 2151
 
0.2%
71 609
 
0.1%
70.17 3972
0.4%
70.08 429
 
< 0.1%
69.75 8123
0.8%
69.5 6870
0.6%
69.17 4788
0.4%
68.25 6365
0.6%

Seniority
Real number (ℝ)

Distinct175
Distinct (%)< 0.1%
Missing7196
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean11.347275
Minimum0
Maximum44
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size16.5 MiB
2024-04-16T16:09:21.533980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.32
Q15.13
median9.39
Q315.57
95-th percentile28.75
Maximum44
Range44
Interquartile range (IQR)10.44

Descriptive statistics

Standard deviation8.6402709
Coefficient of variation (CV)0.76144014
Kurtosis1.4465405
Mean11.347275
Median Absolute Deviation (MAD)5.7
Skewness1.209368
Sum12193646
Variance74.654281
MonotonicityNot monotonic
2024-04-16T16:09:21.662980image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.13 41241
 
3.8%
15.79 32603
 
3.0%
9.18 22678
 
2.1%
15.36 19415
 
1.8%
2.87 19235
 
1.8%
25.08 17690
 
1.6%
28.75 16623
 
1.5%
1.7 14872
 
1.4%
15.41 14611
 
1.4%
9.68 14273
 
1.3%
Other values (165) 861347
79.6%
ValueCountFrequency (%)
0 6
 
< 0.1%
0.01 133
 
< 0.1%
0.02 45
 
< 0.1%
0.03 36
 
< 0.1%
0.04 12
 
< 0.1%
0.05 17
 
< 0.1%
0.08 174
 
< 0.1%
0.09 73
 
< 0.1%
0.1 136
 
< 0.1%
0.18 3359
0.3%
ValueCountFrequency (%)
44 3972
 
0.4%
41.91 609
 
0.1%
37.5 9172
0.8%
37.22 11527
1.1%
36.9 10070
0.9%
31.46 4861
 
0.4%
29.4 11203
1.0%
28.75 16623
1.5%
28.27 429
 
< 0.1%
26.78 5360
 
0.5%

Interactions

2024-04-16T16:09:06.457227image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:53.252199image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:55.354318image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:57.478094image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:59.564087image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:01.823074image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:04.426962image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:06.740500image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:53.534204image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:55.610882image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:57.743153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:59.841117image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:02.198130image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:04.689865image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:07.009783image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:53.826269image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:55.871941image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:58.025153image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:00.160185image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:02.563165image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:04.957850image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:07.288092image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:54.146685image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:56.169467image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:58.281170image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:00.425408image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:02.980826image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:05.269569image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:07.679516image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:54.512215image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:56.568515image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:58.692517image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:00.840729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:03.398002image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:05.623111image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:07.942559image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:54.806839image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:56.889222image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:59.005579image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:01.169729image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:03.793528image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:05.910159image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:08.225904image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:55.096781image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:57.215066image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:08:59.306710image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:01.447258image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:04.159852image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-04-16T16:09:06.202529image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Missing values

2024-04-16T16:09:08.656150image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-16T16:09:10.247489image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OrderNumberCUSTIDJOBTYPEDepartmentFROMLOCSTRTOLOCSTRSTARTTMENDTMTripDurationCTCOMPLETEDTDriverIdPrimeMoverIdTrailerIdJOINED DATERESIGNED DATERACETeamStatusAGE_YearsSeniority
0TL900845300-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-07 08:00:002021-01-07 08:30:000.52021-01-0722151538.02007-06-072021-02-23CHINESECHIEN LI16.053.5813.72
1TL900845300-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-07 10:30:002021-01-07 11:00:000.52021-01-072215972.02007-06-072021-02-23CHINESECHIEN LI16.053.5813.72
2TL900910500-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-27 08:00:002021-01-27 08:30:000.52021-01-272215972.02007-06-072021-02-23CHINESECHIEN LI6.053.5813.72
3TL900910500-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-27 10:30:002021-01-27 11:00:000.52021-01-2722151538.02007-06-072021-02-23CHINESECHIEN LI6.053.5813.72
4TL900855000-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-11 08:00:002021-01-11 08:30:000.52021-01-1122151538.02007-06-072021-02-23CHINESECHIEN LI13.053.5813.72
5TL900855000-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-11 10:30:002021-01-11 11:00:000.52021-01-112215972.02007-06-072021-02-23CHINESECHIEN LI13.053.5813.72
6TL900847400-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-08 08:00:002021-01-08 08:30:000.52021-01-082215972.02007-06-072021-02-23CHINESECHIEN LI12.053.5813.72
7TL900847400-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-08 10:30:002021-01-08 11:00:000.52021-01-0822151538.02007-06-072021-02-23CHINESECHIEN LI12.053.5813.72
8TL900831200-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-04 12:30:002021-01-04 13:00:000.52021-01-042215972.02007-06-072021-02-23CHINESECHIEN LI13.053.5813.72
9TL900905200-RE001LOCALCHIENJI DRUM1 SERAYA AVE2021-01-26 10:30:002021-01-26 11:00:000.52021-01-262215972.02007-06-072021-02-23CHINESECHIEN LI24.053.5813.72
OrderNumberCUSTIDJOBTYPEDepartmentFROMLOCSTRTOLOCSTRSTARTTMENDTMTripDurationCTCOMPLETEDTDriverIdPrimeMoverIdTrailerIdJOINED DATERESIGNED DATERACETeamStatusAGE_YearsSeniority
1222391XI700762000-RE114IMPORTXINHUAPSA/BT/KT2 SERAYA P2023-12-15 07:30:002023-12-15 10:00:002.5000002023-12-153983391800.02015-11-022023-12-31CHINESETeam 18.040.758.16
1222392XI700762000-RE114IMPORTXINHUA2 SERAYA P31 GUL CRE2023-12-20 09:00:002023-12-20 10:00:001.0000002023-12-201692961800.02007-12-012023-12-31CHINESETeam 19.055.3316.08
1222393XI700762000-RE114IMPORTXINHUA31 GUL CRESTOLT NEL2023-12-26 09:30:002023-12-26 10:30:001.0000002023-12-2639833948.02015-11-022023-12-31CHINESETeam 17.040.758.16
1222394XE704654600-RM123EXPORTXINHUAJOOKOONCRPSA/PPT2023-12-01 08:00:002023-12-01 09:45:001.7500002023-12-015083421300.02022-07-052023-12-31MYANMARTeam 68.049.581.49
1222396TE803744200-RA186EXPORTMAIN42PANDAN1PSA/BT/KT2023-12-09 07:00:002023-12-09 08:00:001.0000002023-12-0942551772.02004-02-042023-12-31CHINESEFCL7.055.7519.90
1222397TE803744200-RA186EXPORTMAINPTC48(G)42PANDAN12023-12-06 09:50:002023-12-06 10:00:000.1666672023-12-062202221772.02008-03-172023-12-31CHINESEFCL34.051.3315.79
1222398TL905045000-RA186LOCALCHIENCOGENT TANK 1548DRUMMING2023-12-04 10:30:002023-12-04 12:00:001.5000002023-12-0437145119.02004-06-252023-12-31CHINESECHIEN LI8.068.2519.52
1222399TL905045000-RA186LOCALCHIEN48DRUMMINGSTOLT NEL2023-12-06 05:39:002023-12-06 05:59:000.3333332023-12-06302394119.02008-08-222023-05-10CHINESECHIEN LI21.039.0814.71
1222401TE803752100-RA186EXPORTMAIN42PANDAN1PSA/PPT2023-12-13 09:30:002023-12-13 10:30:001.0000002023-12-131104241937.02000-11-202022-10-02CHINESEFCL8.058.9221.86
1222402XE704751900-RP036EXPORTXINHUACONT CONNPTC MER-GROUND2023-12-29 00:00:002023-12-29 00:00:000.0000002023-12-2901001NaNNaT2023-12-31NaNNaNNaNNaNNaN

Duplicate rows

Most frequently occurring

OrderNumberCUSTIDJOBTYPEDepartmentFROMLOCSTRTOLOCSTRSTARTTMENDTMTripDurationCTCOMPLETEDTDriverIdPrimeMoverIdTrailerIdJOINED DATERESIGNED DATERACETeamStatusAGE_YearsSeniority# duplicates
415XE701718100-RP036EXPORTXINHUACHUAN LI YPTCISL OFF2021-10-192021-10-190.02021-10-1901001NaNNaT2023-12-31NaNNaNNaNNaNNaN28
675XE702411200-RP036EXPORTXINHUAPTCISL OFFPPT2022-03-282022-03-280.02022-03-2801001NaNNaT2023-12-31NaNNaNNaNNaNNaN28
694XE702438700-RP036EXPORTXINHUAPTCISL OFFPPT2022-03-282022-03-280.02022-03-2801001NaNNaT2023-12-31NaNNaNNaNNaNNaN28
311XE701549400-RP036EXPORTXINHUAPTCISL OFFPPT2021-10-062021-10-060.02021-10-0601001NaNNaT2023-12-31NaNNaNNaNNaNNaN25
640XE702289500-RP036EXPORTXINHUAPTCISL OFFPPT2022-03-022022-03-020.02022-03-0201001NaNNaT2023-12-31NaNNaNNaNNaNNaN23
682XE702425900-RP036EXPORTXINHUAPTCISL OFFPPT2022-03-282022-03-280.02022-03-2801001NaNNaT2023-12-31NaNNaNNaNNaNNaN22
680XE702425900-RP036EXPORTXINHUA47JLNBUROHPTCISL OFF2022-03-252022-03-250.02022-03-2501001NaNNaT2023-12-31NaNNaNNaNNaNNaN20
681XE702425900-RP036EXPORTXINHUA47JLNBUROHPTCISL OFF2022-03-282022-03-280.02022-03-2801001NaNNaT2023-12-31NaNNaNNaNNaNNaN20
1034XE703392900-RP036EXPORTXINHUAHLA LOGISTPTCISL OFF2022-12-052022-12-050.02022-12-0501001NaNNaT2023-12-31NaNNaNNaNNaNNaN20
1035XE703393000-RP036EXPORTXINHUAHLA LOGISTPTCISL OFF2022-12-072022-12-070.02022-12-0701001NaNNaT2023-12-31NaNNaNNaNNaNNaN20